Open Access
MATEC Web of Conferences
Volume 55, 2016
2016 Asia Conference on Power and Electrical Engineering (ACPEE 2016)
Article Number 06003
Number of page(s) 6
Section Dynamic Load Modelling and Renewable Energy System
Published online 25 April 2016
  1. J W Taylor, Short-term electricity demand forecasting using double seasonal exponential smoothing. J Oper Res Soc, 54 (8):799–805, (2003). [Google Scholar]
  2. J.W. Taylor and P.E. McSharry, Short-term load forecasting methods: An evaluation based on european data. Power Systems, IEEE Transactions on, 22 (4):2213–2219, (2007). [Google Scholar]
  3. John Peirson and Andrew Henley, Electricity load and temperature: Issues in dynamic specification. Energy Economics, 16(4):235–243, (1994). [CrossRef] [Google Scholar]
  4. HarveyAndrew and Siem Jan Koopman, Forecasting hourly electricity demand using time-varying splines. Journal of the American Statistical Association, 88 (424):1228–1236, (1993) [CrossRef] [Google Scholar]
  5. Michael Smith, Modeling and short-term forecasting of new south wales electricity system load. Journal of Business & Economic Statistics, 18 (4):465–478, (2000). [Google Scholar]
  6. Ramu Ramanathan, Robert Engle, Clive W. J. Granger, Farshid Vahid-Araghi, and Casey Brace. Short-run forecasts of electricity loads and peaks. International Journal for Forecasting, (13):161–174, (1997) [CrossRef] [Google Scholar]
  7. R. Cottet and Michael Stanley Smith, Bayesian modeling and fore-casting of intraday electricity load. Journal of the American Statistical Association , 98:839–849, (2003). [CrossRef] [Google Scholar]
  8. Lacir J. Soares and Marcelo C. Medeiros, Modeling and forecasting short-term electricity load: A comparison of methods with an application to brazilian data. International Journal of Forecasting, 24(4):630–644, (2008). [Google Scholar]
  9. V. Dordonnat, S.J. Koopman, M. Ooms, A. Dessertaine, and J. Collet. An Hourly Periodic State Space Model for Modelling French National Electricity Load. Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute, (2008). [Google Scholar]
  10. J.W. Taylor and R. Buizza,Neural network load forecasting with weather ensemble predictions. Power Systems, IEEE Transactions on, 17(3):626–632, (2002). [CrossRef] [Google Scholar]
  11. H.S. Hippert, C.E. Pedreira, and R.C. Souza, Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Systems, 16(1):44–55, (2001). [Google Scholar]
  12. Karin Kandananond, Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach.Energies, 4(8):1246–1257, (2011). [CrossRef] [Google Scholar]
  13. Seyyed Mohammad Mousavi, Elham Sadat Mostafavi, and Fariba Hosseinpour. Gene expression programming as a basis for new generation of electricity demand prediction models. Comput. Ind. Eng., 74:120–128, (2014). [CrossRef] [Google Scholar]

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